New opportunity

Staff AI Engineer

Palo Alto Networks · Bangalore, India

About this role

As a Staff AI Engineer at Palo Alto Networks, you will design, build, and support enterprise-grade software solutions that power the company's internal teams and operations. You will transform complex business needs into scalable, secure, and reliable systems by leveraging AI-first engineering practices. You will own the full project lifecycle, from architecture and design to deployment, observability, and continuous improvement, ensuring IT systems are prepared for an AI-driven future. Responsibilities: Partner with Product Managers and business stakeholders to translate enterprise requirements into technical solutions. Apply AI-first engineering capabilities, including prompt engineering, AI-assisted coding, and agentic workflows, to accelerate development and operational support. Design and implement software solutions across IT platforms using modern architectures. Develop high-quality code using AI-augmented tools like Cursor and Cody while validating for security and compliance. Debug and resolve system issues using AI-assisted diagnostics. Participate in rigorous code reviews and contribute to automation in a DevOps/CI-CD environment. Qualifications: 7–10 years of hands-on software engineering experience using core First Principles AI. Proficiency in modern programming languages such as Python, JavaScript/TypeScript, Java, or Go. Strong understanding of LLMs, MCP, and infrastructure requirements. Experience with SDLC, DevOps/DevSecOps foundations, and version control. Must have experience with large language models (LLMs), building AI agents or AI/ML, and integrating custom applications with 3rd party systems like SAP, SFDC, or Workday. Familiarity with cloud environments (GCP, AWS, or Azure) and containerized development (Docker, Kubernetes) is required. Exposure to enterprise SaaS platforms and modern developer tools is expected. The role is based in Bangalore, India, and requires working from the office full-time. Please note that immigration sponsorship is not available for this position.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonJavaScriptTypeScriptJavaGoLLMsMCPPrompt EngineeringAI-assisted codingAgentic workflowsCursorCodyClaudeGCPAWSAzureDockerKubernetesSalesforceSAPServiceNowCI/CDDevOpsDevSecOpsGit

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

AI-First Engineering Integration
The role requires embedding AI-assisted coding, prompt engineering, and agentic workflows into the full software development lifecycle. You must demonstrate how to use tools like Cursor or Cody to accelerate development while maintaining high code quality and system reliability.
Enterprise System Integration
You will be responsible for integrating custom AI applications with complex, third-party enterprise SaaS platforms such as SAP, Salesforce, and Workday. Success depends on your ability to bridge modern AI capabilities with established, mission-critical business systems.
Full-Lifecycle Ownership
As a Staff-level engineer, you must own projects from initial architecture and design through to deployment, observability, and continuous improvement. This requires balancing high-level technical strategy with hands-on DevOps and CI/CD automation.

Likely challenges to think through

Validating AI-Generated Code for Security
While the role mandates the use of AI-augmented coding tools, you are ultimately responsible for security and compliance. Prepare to discuss your methodology for auditing AI-generated code to ensure it meets enterprise security standards and does not introduce vulnerabilities.
Managing Technical Debt in AI-Driven Workflows
Rapidly deploying AI-assisted solutions can lead to maintenance challenges. You will need to demonstrate how you maintain system observability and long-term reliability when using automated workflows, ensuring that speed does not compromise system stability.

Practice your answers

Technical practice

Architecting AI-Agentic Workflows

What to coverExplain how you design and deploy agentic workflows within an enterprise environment. Focus on how you handle state management, tool selection, and error recovery when integrating agents with complex systems like SAP or Salesforce.

LLM Integration and Security

What to coverDiscuss the security and compliance challenges of integrating LLMs into internal IT systems. Address how you validate AI-generated code and outputs to ensure they meet enterprise security standards and prevent data leakage.

AI-Assisted Development Lifecycle

What to coverDescribe your experience using AI-augmented tools like Cursor or Cody in a professional SDLC. Explain how you balance the speed of AI-assisted coding with the need for rigorous code reviews and maintainable, high-quality software.

Scaling AI Solutions on Cloud Infrastructure

What to coverDetail your approach to deploying AI-driven applications on cloud platforms like GCP, AWS, or Azure. Focus on containerization strategies using Docker and Kubernetes to ensure scalability, observability, and reliability.

Integrating with Enterprise SaaS Platforms

What to coverProvide an example of how you have integrated custom AI applications with third-party systems such as Workday or Salesforce. Discuss the trade-offs between using native APIs versus custom middleware for these integrations.

DevOps and CI/CD for AI Systems

What to coverExplain how you incorporate AI-driven diagnostics and automation into a CI/CD pipeline. Suggestion: Focus on how you maintain observability and automated testing for non-deterministic AI components.

First Principles AI Engineering

What to coverDefine what 'First Principles AI' means to you in the context of software engineering. Explain how this mindset influences your choice of models, frameworks, and architectural patterns when building new systems.

Troubleshooting AI-Driven Systems

What to coverDescribe your methodology for debugging complex system issues where AI components are involved. Explain how you differentiate between model-level failures and traditional infrastructure or logic errors.

Behavioural practice

Translating Business Needs to Technical Solutions

What to coverDescribe a situation where you had to translate complex requirements from non-technical stakeholders (e.g., HR or Finance) into a scalable technical architecture. Focus on how you ensured the solution aligned with strategic business goals.

Driving Innovation in a Team

What to coverGive an example of how you have introduced or advocated for new AI-first engineering practices within a team. Explain how you managed resistance to change and fostered a growth mindset among your peers.

Handling Project Lifecycle Ownership

What to coverShare an experience where you owned a project from architecture to deployment and continuous improvement. Highlight how you managed trade-offs between speed of delivery and long-term system reliability.

Collaborating Across Functional Teams

What to coverDescribe a time you worked with cross-functional teams like R&D or Global Customer Support to solve a critical operational problem. Explain how you communicated technical constraints while maintaining a collaborative relationship.

Sharpen your knowledge

Topics to prepare

Agentic Workflow Architecture
Focus on designing systems where LLMs act as autonomous agents to perform multi-step tasks. Exercise: Draft an architecture diagram for an AI agent that automates a common IT support ticket workflow, specifically integrating with systems like Salesforce or ServiceNow to retrieve data and execute actions.
AI-First Software Development Lifecycle (SDLC)
Understand how to integrate AI tools into every stage of the SDLC, from requirements gathering to deployment and observability. Exercise: Map out a standard CI/CD pipeline and identify specific points where AI-assisted diagnostics and automated code validation can be inserted to improve security and compliance.
Model Context Protocol (MCP) Integration
Study the Model Context Protocol to understand how to standardize the connection between AI models and enterprise data sources. Exercise: Review the MCP specification and document how you would use it to securely expose internal SAP or Workday data to an LLM-based application.

Skills to revise

AI-Augmented Coding with Cursor and Cody
Gain proficiency in using AI-native IDEs to accelerate development and refactoring. Exercise: Take an existing piece of legacy code and use Cursor or Cody to refactor it for better performance, while manually verifying the security and logic of the AI-generated output.
Enterprise SaaS Integration
Practice integrating custom applications with enterprise platforms like Salesforce, SAP, or Workday. Exercise: Build a small Python script that authenticates with a sandbox environment of a SaaS platform and performs a CRUD operation, ensuring the code follows secure API practices.
Containerized Deployment and Observability
Ensure mastery of deploying AI-driven applications using Docker and Kubernetes. Exercise: Create a Dockerfile for a Python-based LLM service and define a Kubernetes deployment manifest that includes liveness and readiness probes to ensure system reliability.

Your preparation checklist

Prepare a portfolio of AI agent projects
Document your experience building AI agents that integrate with enterprise systems like SAP, Salesforce, or Workday. Be ready to explain the architecture, the specific LLMs used, and how you handled data security and compliance in those integrations.
Demonstrate AI-first engineering workflows
Prepare a walkthrough of how you use tools like Cursor or Cody to accelerate development. Explain your process for validating AI-generated code for security, performance, and adherence to enterprise coding standards.
Review DevOps and cloud infrastructure foundations
Refresh your knowledge on deploying containerized applications using Docker and Kubernetes on major cloud platforms (AWS, GCP, or Azure). Be prepared to discuss how you implement CI/CD pipelines and observability for AI-driven systems.
Develop questions regarding enterprise AI strategy
Prepare thoughtful questions for the interviewers about how Palo Alto Networks balances rapid AI adoption with the strict security requirements of a cybersecurity company. Ask about the current challenges in scaling agentic workflows across their internal IT functions.
Refine your technical communication for stakeholders
Practice explaining complex technical AI solutions to non-technical business stakeholders. Prepare examples of how you have partnered with Product Managers to translate business needs into scalable, reliable technical architectures.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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